Background: Short Term Load Forecasting (STLF) can predict load from several
minutes to week plays a vital role to address challenges such as optimal generation, economic
scheduling, dispatching and contingency analysis.
Methods: This paper uses Multi-Layer Perceptron (MLP) Artificial Neural Network (ANN) technique
to perform STFL but long training time and convergence issues caused by bias, variance and
less generalization ability, make this algorithm unable to accurately predict future loads.
Results: This issue can be resolved by various methods of Bootstraps Aggregating (Bagging) (like
disjoint partitions, small bags, replica small bags and disjoint bags) which help in reducing variance
and increasing generalization ability of ANN. Moreover, it results in reducing error in the learning
process of ANN. Disjoint partition proves to be the most accurate Bagging method and combining
outputs of this method by taking mean improves the overall performance.
Conclusion: This method of combining several predictors known as Ensemble Artificial Neural
Network (EANN) outperforms the ANN and Bagging method by further increasing the generalization
ability and STLF accuracy.